- Non Stationary Fuzzy Time Series - NSFTS: stable version

pyFTS/common/FLR.py

@@ -52,7 +52,7 @@ def generateRecurrentFLRs(fuzzyData):
                     tmp = FLR(l, r)
                     flrs.append(tmp)
         else:
-            tmp = FLR.FLR(lhs,rhs)
+            tmp = FLR(lhs,rhs)
             flrs.append(tmp)
     return flrs
 

pyFTS/nonstationary/common.py

@@ -221,9 +221,9 @@ class PolynomialNonStationaryPartitioner(partitioner.Partitioner):
     def get_polynomial_perturbations(self, data, **kwargs):
         w = kwargs.get("window_size", int(len(data) / 5))
         deg = kwargs.get("degree", 2)
-        xmax = [0]
+        xmax = [data[0]]
         tmax = [0]
-        xmin = [0]
+        xmin = [data[0]]
         tmin = [0]
         lengs = [0]
         tlengs = [0]

pyFTS/nonstationary/nsfts.py

@@ -76,11 +76,21 @@ class NonStationaryFTS(fts.FTS):
 
         for k in np.arange(0, l):
 
+            #print("input: " + str(ndata[k]))
+
             tdisp = k + time_displacement
 
             affected_sets = [ [set, set.membership(ndata[k], tdisp)]
                               for set in self.sets if set.membership(ndata[k], tdisp) > 0.0]
 
+            if len(affected_sets) == 0:
+                if self.sets[0].get_lower(tdisp) > ndata[k]:
+                    affected_sets.append([self.sets[0], 1.0])
+                elif self.sets[-1].get_upper(tdisp) < ndata[k]:
+                    affected_sets.append([self.sets[-1], 1.0])
+
+            #print(affected_sets)
+
             tmp = []
             for aset in affected_sets:
                 if aset[0] in self.flrgs:
@@ -88,7 +98,11 @@ class NonStationaryFTS(fts.FTS):
                 else:
                     tmp.append(aset[0].get_midpoint(tdisp) * aset[1])
 
-            ret.append(sum(tmp))
+            pto = sum(tmp)
+
+            #print(pto)
+
+            ret.append(pto)
 
         ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
 

pyFTS/nonstationary/util.py

@@ -15,13 +15,19 @@ def plot_sets(uod, sets, start=0, end=10, tam=[5, 5], colors=None, save=False, f
             param = set.perturbated_parameters[t]
 
             if set.mf == Membership.trimf:
-                axes.plot([t, t+1, t], param)
+                if t == start:
+                    axes.plot([t, t+1, t], param, label=set.name)
+                else:
+                    axes.plot([t, t + 1, t], param)
 
         ticks.extend(["t+"+str(t),""])
 
     axes.set_ylabel("Universe of Discourse")
     axes.set_xlabel("Time")
-    plt.xticks([k for k in np.arange(0,2*end,1)], ticks, rotation='vertical')
+    plt.xticks([k for k in np.arange(0,end,1)], ticks, rotation='vertical')
+
+    handles0, labels0 = axes.get_legend_handles_labels()
+    lgd = axes.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
 
     plt.tight_layout()
 

pyFTS/partitioners/partitioner.py

@@ -79,4 +79,4 @@ class Partitioner(object):
                 ax.plot(tmpx, tmpy)
 
     def __str__(self):
-        return self.name
+        return self.name + ":\n ".join([str(a) + "\n"  for a in self.sets])

pyFTS/tests/nonstationary.py

@@ -1,10 +1,13 @@
+import os
 import numpy as np
 from pyFTS.common import Membership
 from pyFTS.nonstationary import common,perturbation,util,nsfts
 from pyFTS.partitioners import Grid
 import matplotlib.pyplot as plt
+import pandas as pd
+os.chdir("/home/petronio/Dropbox/Doutorado/Codigos/")
 
-
+"""
 def generate_heteroskedastic_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=10, num=35):
     mu = mu_ini
     sigma = sigma_ini
@@ -17,6 +20,8 @@ def generate_heteroskedastic_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=10,
 
 
 lmv1 = generate_heteroskedastic_linear(1,0.1,1,0.3)
+#lmv1 = generate_heteroskedastic_linear(5,0.1,0,0.2)
+#lmv1 = generate_heteroskedastic_linear(1,0.3,1,0)
 
 ns = 5 #number of fuzzy sets
 ts = 200
@@ -25,19 +30,54 @@ test = lmv1[ts:]
 w = 25
 deg = 4
 
-fig, axes = plt.subplots(nrows=1, ncols=1, figsize=[10,5])
-
 tmp_fs = Grid.GridPartitioner(train[:35], 10)
 
 fs = common.PolynomialNonStationaryPartitioner(train, tmp_fs, window_size=35, degree=1)
 
 nsfts1 = nsfts.NonStationaryFTS("", partitioner=fs)
 
-nsfts1.train(train[:35])
+nsfts1.train(train[:100])
 
-tmp = nsfts1.forecast(test, time_displacement=200)
+print(fs)
 
-axes.plot(test)
+print(nsfts1)
-axes.plot(tmp)
+
+tmp = nsfts1.forecast(test[:10], time_displacement=200)
 
 print(tmp)
+"""
+
+passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
+passengers = np.array(passengers["Passengers"])
+
+ts = 80
+
+trainp = passengers[:ts]
+testp = passengers[ts:]
+
+tmp_fsp = Grid.GridPartitioner(trainp[:50], 10)
+
+fsp = common.PolynomialNonStationaryPartitioner(trainp, tmp_fsp, window_size=20, degree=1)
+
+nsftsp = nsfts.NonStationaryFTS("", partitioner=fsp)
+
+nsftsp.train(trainp[:50])
+
+print(fsp)
+
+print(nsftsp)
+
+tmpp = nsftsp.forecast(testp, time_displacement=ts)
+
+print(testp)
+print(tmpp)
+
+#fig, axes = plt.subplots(nrows=1, ncols=1, figsize=[15,5])
+
+"""
+axes.plot(testp, label="Original")
+#axes.plot(tmpp, label="NSFTS")
+
+handles0, labels0 = axes.get_legend_handles_labels()
+lgd = axes.legend(handles0, labels0, loc=2)
+"""